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The transpose is geometrically defined as the operation that makes these dot products equal: $(Ax)\cdot y=x\cdot ({ A }^{ T }y)$. I have absolutely no idea for the motivation of this definition, or where it even comes from. I've looked at many other answers, but to no avail.

What is the geometric motivation/intuition behind this definition? Why is the transpose defined this way? What is this definition even saying?

For reference, I'm a HS student currently on 2.7 of Introduction to Linear Algebra by Prof. Strang, and those are all the ideas I understand, so please avoid higher-level concepts.

Invisible
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DarkRunner
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  • [This post](https://math.stackexchange.com/q/484844/721644) might be useful. Have you seen it? – Invisible Apr 27 '20 at 23:32
  • @ms._VerkhovtsevaKatya The notation is difficult – DarkRunner Apr 27 '20 at 23:34
  • I'm a student myself, still learning. I'll try to find as many posts as possible with as little complicated notation as posibble. Maybe [this answer](https://math.stackexchange.com/a/2782421/721644)? Or [another one](https://math.stackexchange.com/a/2193276/721644)? – Invisible Apr 27 '20 at 23:36
  • [from Reddit](https://www.reddit.com/r/math/comments/5ro2ky/what_is_the_geometric_interpretation_of_a/) – Invisible Apr 27 '20 at 23:45

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If you accept the definition of $A^T$ as the usual easily visualized definition of flipping $A$ along the diagonal and switching its rows and columns, then that equality follows easily from properties of the dot product: $(Ax) \cdot y = (Ax)^T y = (x^T A^T) y = x^T (A^T y) = x \cdot (A^T y)$, where the second equality comes from the following property of transposes: $(AB)^T = B^T A^T$ for two matrices $A$ and $B$, where $B$ could be a vector since we can think of a vector with $n$ entries as a $n \times 1$ matrix. The third equality comes from associativity of matrix multiplication.

As to why Strang introduces that definition in terms of dot products, I would guess it is partly because the “easier” definition of transpose as “flipping a matrix” might be a little casual from a mathematical standpoint. Moreover, this new definition defines the transpose entirely in terms of the dot product, so there is the benefit of being more algebraic and expressed only in terms of one or two basic operations (dot product and matrix multiplication); it doesn’t appeal to the physical idea of flipping a matrix along the diagonal, which is easy to visualize but maybe hard to see how that relates to the algebra of things and to the basic operations used in linear algebra. Both approaches are important and useful.

Also, as a general remark, the dot product is an example of an inner product (and more generally, bilinear form), which if you haven’t encountered already, you will probably encounter if you study more math/linear algebra. Notions like inner product, inner product spaces, bilinear forms are very important and general concepts in math. So, the transpose can be introduced this way, and this approach may seem alien and esoteric, but is actually more general (and rigorous). Anyways, I don't know too much about that, so I won't comment further.

As for the geometric intuition, I’m not sure if I can answer it better than the top-rated answer in the following post: What is the geometric interpretation of the transpose?. That answer references the singular value decomposition. So, you could wait until Chapter 7 of Strang’s book, in which he talks about the SVD, as well as the geometry of it. Of course, there may be a quicker answer that is still satisfying and doesn’t reference the SVD, but I’m not currently aware of one.

twosigma
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